IYTE GCRIS Database (Izmir Institute of Technology)
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Development of Xylan-Coated Acid-Resistant Micellar Drug Carriers for Colon-Targeted Oral Delivery
No full textOral delivery of hydrophobic drugs from the stomach through the colon has some requirements: (1) an acid-resistant carrier (2) a colon-specific drug release mechanism; and (3) an enhanced bioavailability. In this study, curcumin-loaded polymeric micelles with a xylan-based composite coating were designed and developed. For this purpose, a new synthesis method was used to precipitate xylan by concurrent chitosan polymerization at different xylan/chitosan ratios using a negatively charged crosslinking agent, TPP. The study was to provide the stability of the coated micellar structures in the stomach (low pH conditions) and their degradation in the colon (a natural environment of bacteria) to release the drug. It was observed that the coating successfully prevented early drug release up to 85%, depending on the fraction of xylan in the coating. The nanocarriers that first passed through the stomach conditions were incubated with a xylanolytic colonic bacterium (Bacteroides ovatus) to determine the bacterium-related release mechanism, which was around 27%. This shows the colon-specific release expectation of coated nanocarriers in the colon environment, with an additional benefit due to the degradation of xylan and an improvement in the colon environment by prebiotic activity
Video Surveillance System Based on Action and Event Recognition With Moving Object Detection and Tracking
No full textLojistik sektörünün son yıllarda hızla büyümesi, depo alanlarının genişlemesine ve kullanılan ekipman sayısının artmasına neden olarak iş kazalarının artmasına neden olmuştur. Depolarda meydana gelen iş kazaları çoğunlukla dikkatsizlik, yorgunluk, yoğun iş temposu, bireysel davranışlar, deneyim eksikliği, yetersiz eğitim ve çalışanların ihmalinden kaynaklanmaktadır. Bu nedenle depo içi emniyetin sağlanması için insan ve ekipman etkileşimini gerçek zamanlı olarak tahmin eden bir sisteme ihtiyaç vardır. Tez kapsamında depo ortamlarında iş güvenliğini artıracak nesne algılama, nesne izleme, eylem algılama ve alarm sınıflandırma bileşenlerinden oluşan kapsamlı bir video gözetim sistemi önerilmektedir. Bu sistemde nesne tespit metodolojisi olarak kullanılan YOLOv7, nesneleri tek bir ağ geçişinde hızlı ve doğru bir şekilde tespit eden bir derin öğrenme modelidir. Deep SORT ise izlenen her nesneye benzersiz bir tanımlayıcı atayan ve izleme sırasında derin öğrenmeyi kullanan bir bilgisayarlı görme izleme teknolojisidir. Sistemin eylem algılama kısmı, anormallikleri ve potansiyel riskleri tanıyarak eylemleri ve hareketleri tanımlamak ve analiz etmek için tasarlanmıştır. Bu bölümde insan ve ekipmanların hız, etiket, hareket yönü ve koordinat bilgileri kullanılarak çeşitli alarm seviyeleri tahmin edilmekte ve bu tahmini alarm seviyelerine bağlı olarak da farklı alarm seviyeleri üretilmektedir. Gerçek zamanlı müdahale ve yüksek başarı oranıyla çalışabilme gibi teknolojik yeterlilikleri sağlaması test edilen bu sistem sayesinde depolardaki kazalar tahmin edilecek, alarmlar üretilecek ve olası iş kazaları büyük ölçüde önlenebilecektir.The rapid growth of the logistics sector in recent years has led to the expansion of warehouse areas and an increase in the number of equipment used, resulting in an increase in work accidents. Work accidents that occur in warehouses are mostly caused by carelessness, fatigue, intense work tempo, individual behavior, lack of experience, inadequate training, and negligence of employees. Therefore, a system that predicts person-equipment interaction in real time is needed to ensure in-warehouse reliability. Within the scope of the thesis, a comprehensive video surveillance system consisting of object detection, object tracking, action detection, and alarm classification components that will increase occupational safety in warehouse environments is proposed. YOLOv7, used as the object detection methodology in this system, is a deep learning model that detects objects quickly and accurately in a single network pass. Deep SORT is a computer vision tracking procedure that assigns a unique identifier to each tracked object and uses deep learning during tracking. The action detection part of the system analyzes identifies actions and movements and recognizes anomalies and potential risks. Then, various alarm levels are estimated using the speed, tag, movement direction, and coordinate information of the person and equipment, and different alarm levels are generated depending on these estimated alarm levels. Through this system, which has been tested to provide technological competencies such as real-time response and to work with a high success rate, accidents in warehouses will be predicted, alarms will be generated, and possible occupational accidents can be prevented to a large extent
Sioc Foam-Aerogel Composites: Optimal Balance of Lightness and Excellent Thermal Insulation
No full textFoam-aerogel composites are synthesized in polymeric, hybrid, and ceramic states by completing the open cells of the foam with a solution forming a wet gel, carbon dioxide (CO2) supercritically dried, and pyrolyzed. Thermal diffusivity measurements are conducted using the laser flash, and for mechanical performance, cold crushing tests are done to obtain compressive strengths. Samples possess a range of specific surface area (SSA) values up to similar to 650 m2/g contingent upon the material state, that is, polymeric, hybrid, or ceramic. While SSA values can be deliberately altered, almost all samples demonstrated a total porosity of similar to 90 vol%, with superb specific compressive strength reaching around 2 MPa. In addition to adjustable surface characteristics granting hydrophobic and hydrophilic features, the study revealed the potential use of these foam-aerogel composites as thermal insulators with low thermal conductivities of 0.02 Wmiddle dot>m-1middle dot>K-1 at RT and 0.05 Wmiddle dot>m-1middle dot>K-1 at 500 degrees C. When exposed directly to a butane flame gun with a flame temperature reaching similar to 1200 degrees C, from the backside of a 5 mm-thick foam-aerogel composite, only similar to 200 degrees C is recorded, which is lower than a comparable commercial insulator panel tested under the same conditions
Prediction of Associations Between Nanoparticle, Drug and Cancer Using Variational Graph Autoencoder
No full textPredicting implicit drug-disease associations is critical to the development of new drugs, with the aim of minimizing side effects and development costs. Existing drug-disease prediction methods typically focus on either single or multiple drug-disease networks. Recent advances in nanoparticles particularly in cancer research show improvements in bioavailability and pharmacokinetics by reducing toxic side effects. Thus, the interaction of the nanoparticles with drugs and diseases tends to improve during the development phase. In this study, it presents a variational graph autoencoder model to the cell-specific drug delivery data, including the class interactions between nanoparticle, drug, and cancer types as a knowledge base for targeted drug delivery. The cell-specific drug delivery data is transformed into a bipartite graph where relations only exist between sequences of these class interactions. Experimental results show that the knowledge graph enhanced Variational Graph Autoencoder model with VGAE-ROC-AUC (0.9627) and VGAE-AP (0.9566) scores performs better than the Graph Autoencoder model
Phosphate Recovery From Digestate Using Magnesium-Modified Fungal Biochar
No full textMg-rich biochars have been used for the removal and recovery of phosphate (PO43-) and ammonium (NH4+) from waste streams. In this study, a novel magnesium-modified biochar (Mg-FBC) was synthesized by immobilizing waste magnesite dust (WMD) into Aspergillus niger fungal biomass for the adsorption of PO(4)(3- )and NH4+. Pyrolysis at various temperatures and analysis using techniques such as SEM-EDS, TGA, XRD, FTIR, and BET revealed that biochar produced at 650 degrees C (Mg-FBC650) exhibited enhanced surface properties favorable for effective adsorption. This improvement is attributed to the increased surface area facilitated by the hyphal structure of A. Niger and the effective dispersion of MgO on its surface. In experiments using a synthetic phosphate solution, the adsorption capacity reached 595 mg PO43-/g BC, fitting the Langmuir model at pH 9. In addition, experiments with the liquid fraction of a real digestate (LFD) showed adsorption capacities of 502 mg PO43-/g BC and 150 mg NH4+/g BC, respectively. The adsorption mechanism was elucidated through SEM-EDS, XRD, and FTIR analyses confirming that Mg-FBC650 facilitates a multifaceted adsorption mechanism, including adsorption, electrostatic attraction, chemical precipitation, and surface complexation. Consequently, PO43- was the primary adsorbate in the synthetic solution, while both PO43- and NH4+ were effectively removed from the LFD, indicating that Mg-FBC650 has substantial potential as an efficient adsorbent for nutrient removal. As a result, Mg-FBC650 is believed to hold significant potential as a slow-release and readily transferable bio-fertilizer, particularly suitable for application in soils deficient in organic matter, nitrogen, and phosphorus. [GRAPHICS]
Secrecy performance of full-duplex space-air integrated networks in the presence of active/passive eavesdropper, and friendly jammer
No full textIn this paper, a full-duplex (FD) space-air ground integrated network (SAGIN) system with passive and active eavesdroppers (PE/AE) and a friendly jammer (FJ) is investigated. The shadowing side information (SSI)-based unmanned aerial vehicle relay node (URN) selection strategy is considered to improve signal-to-interference plus noise power ratio (SINR) at the ground destination unit. To quantify the secrecy performance of the considered scenario, outage probability (OP), interception probability (IP), and transmission secrecy outage probability (TSOP) are investigated in the presence of FJ and PE/AE. The results have shown that aerial AE is an important threat since it can severely degrade the OP of the main transmission link. Furthermore, the FJ can decrease the IP of the eavesdropper by causing interference with the cost of power consumption of URNs. Simulations are performed to verify the theoretical findings
Stochastic 1-D Reactive Transport Simulations To Assess Silica and Carbonate Phases During the Reinjection Process in Metasediments
No full textOne proposed method to mitigate carbon emission is to mineralize the in deep geothermal reservoirs while mixing the coproduced CO2 with the effluent fluid for reinjection. The injection fluid temperature fluctuates due to the mixing process between CO2-charged water and the effluent fluid, and compressor interruptions change the thermodynamic conditions that influence the fluid- rock interaction in the reservoir. Mineral dissolution or precipitations are associated with changes in permeability and porosity that affect the flow and, eventually, the lifespan of the reservoir. A combined stochastic–reactive transport simulation approach is useful for inspection purposes. Moreover, the stochastic algorithm validates the deterministic reactive transport simulation and demonstrates the time evolution of a chemically reacting system in the reservoir. This study examines a range of injection temperatures between 80 °C and 120 °C to evaluate silica and calcite precipitation along a flow path. One-dimensional (1-D) reactive transport and compartment- based stochastic reaction-diffusion-advection Gillespie algorithms are carried out. The 1-D model represents a reservoir feed zone of around 2300 m. Two common metasediment rock types are evaluated for inspection. The first one is the muscovite schist, which has approximately 60% quartz, and the second is the quartz schist, consisting of roughly 90% quartz. The stochastic method can be applied more effectively if the chemical system is completely defined with proper reaction rates as a function of temperature. The mixing ratio of the coproduced over the effluent fluid is around 0.0028. Simulation results show that is partially sequestrated as calcite within the first 10 m of the entrance to the reservoir and plugs the pores completely in the muscovite schist scenario. Chalcedony and α-cristobalite precipitate as secondary minerals evenly along the flow path. injection into a quartz schist layer is more appropriate for geochemical interactions below 120 °C
An Investigation of Rna Methylations With Biophysical Approaches in a Cervical Cancer Cell Model
No full textRNA methylation adds a second layer of genetic information that dictates the post-transcriptional fate of RNAs. Although various methods exist that enable the analysis of RNA methylation in a site-specific or transcriptome-wide manner, whether biophysical approaches can be employed to such analyses is unexplored. In this study, Fourier-transform infrared (FT-IR) and circular dichroism (CD) spectroscopy are employed to examine the methylation status of both synthetic and cellular RNAs. The results show that FT-IR spectroscopy is perfectly capable of quantitatively distinguishing synthetic m(6)A-methylated RNAs from un-methylated ones. Subsequently, FT-IR spectroscopy is successfully employed to assess the changes in the extent of total RNA methylation upon the knockdown of the m(6)A writer, METTL3, in HeLa cells. In addition, the same approach is shown to accurately detect reduction in total RNA methylation upon the treatment of HeLa cells with tumor necrosis factor alpha (TNF-alpha). It is also demonstrated that m(1)A and m(6)A methylation induce quite a distinct secondary structure on RNAs, as evident from CD spectra. These results strongly suggest that both FT-IR and CD spectroscopy methods can be exploited to uncover biophysical properties impinged on RNAs by methyl moieties, providing a fast, convenient and cheap alternative to the existing methods
A Sustainable Clean Energy Source for Mitigating Cosub>2/Sub> Emissions: Numerical Simulation of Hamit Granitoid, Central Anatolian Massif
No full textT ; uuml;rkiye relies on coal-fired power plants for approximately 18 GW of annual electricity generation, with significantly higher CO2 emissions compared to geothermal power plants. On the other hand, geothermal energy resources, such as Enhanced Geothermal Systems (EGS) and hydrothermal systems, offer low CO2 emissions and baseload power, making them attractive clean energy sources. Radiogenic granitoid, with high heat generation capacity, is a potential and cleaner energy source using EGS. The Anatolian plateau hosts numerous tectonic zones with plutonic rocks containing high concentrations of radioactive elements, such as the Central Anatolian Massif. This study evaluates the power generation capacity of the Hamit granitoid (HG) and presents a thermo-hydraulic-mechanical (THM) model for a closed-loop geothermal well for harnessing heat from this granitoid. A sensitivity analysis based on fluid injection rates and wellbore length emphasizes the importance of fluid resident time for effective heat extraction. Closed-loop systems pose fewer geomechanical risks than fractured systems and can be developed through site selection, system design, and monitoring. Geothermal wellbore casing material must withstand high temperatures, corrosive environments, and should have low thermal conductivity. The HG exhibits the highest heat generation capacity among Anatolian granitoid intrusions and offers potential for sustainable energy development through EGS, thereby reducing CO2 emissions
Recommendations for Measuring and Standardizing Light for Laboratory Mammals To Improve Welfare and Reproducibility in Animal Research
No full textLight enables vision and exerts widespread effects on physiology and behavior, including regulating circadian rhythms, sleep, hormone synthesis, affective state, and cognitive processes. Appropriate lighting in animal facilities may support welfare and ensure that animals enter experiments in an appropriate physiological and behavioral state. Furthermore, proper consideration of light during experimentation is important both when it is explicitly employed as an independent variable and as a general feature of the environment. This Consensus View discusses metrics to use for the quantification of light appropriate for nonhuman mammals and their application to improve animal welfare and the quality of animal research. It provides methods for measuring these metrics, practical guidance for their implementation in husbandry and experimentation, and quantitative guidance on appropriate light exposure for laboratory mammals. The guidance provided has the potential to improve data quality and contribute to reduction and refinement, helping to ensure more ethical animal use. Lighting conditions for laboratory mammals is currently set according to the sensitivity of human vision. This Consensus View defines alternative 'animal-centric' metrics and provides guidance for their application to standardize experimental conditions, improve animal welfare and the quality of animal research